District-HeatIndex-Predication-using-LSTM
Model | Feature No. | Test Loss | Test Accuracy | Predicted HEAT_INDEX | Actual HEAT_INDEX | Mean Absolute Error |
---|---|---|---|---|---|---|
1 | 8 | 1.52 | 90.45 | 73.28 | 74.71 | 1.43% |
2 | 4 | 1.50 | 90.16 | 74.79 | 74.71 | 0.08% |
3 | 25 | 0.34 | 40.56 | 73.9 | 74.71 | 0.91% |
- model 2 shows best performance as its Mean Absolute Error is very less than Model 1 and Model 3...
- Model 1 :
['T2M', 'T_Fahrenheit', 'TS', 'T2M_MIN', 'T2M_MAX', 'T2MWET', 'WS10M_RANGE', 'DISTRICT_INDEX']
- Model 2 :
['T2M','RH2M', 'T2MDEW', 'WD10M']
- Model 3 :
['T2M', 'T_Fahrenheit', 'TS', 'T2M_MIN', 'T2M_MAX', 'T2MWET', 'WS10M_RANGE', 'WD50M', 'WD10M', 'WS10M_MAX', 'RH2M', 'MO', 'WS10M', 'T2MDEW', 'QV2M', 'WS50M', 'LON', 'DISTRICT_INDEX', 'LAT', 'PS', 'PRECTOTCORR', 'YEAR', 'T2M_RANGE', 'DY', 'WS10M_MIN']
HeatIndex Prediction using LSTM
LSTM models predict Heat Index with varying feature sets. Model 2 excels, showcasing superior performance with fewer features.
Predict Heat Index based on meteorological features for improved understanding of weather conditions and human comfort.
This project utilizes LSTM models to predict Heat Index, comparing performance with different feature sets. Model 2, with 4 selected features, outperforms others.
- Weather Forecasting
- Urban Planning
- Health and Safety
- Agriculture
- Energy Consumption
- Outdoor Event Planning
- Accurate Heat Index Prediction
- Reduced Model Complexity (Model 2)
- Better Interpretability (Model 2)
- Faster Training and Inference (Model 2)
- Improved Resource Efficiency
- Limited Feature Set
- Potential Overfitting
- Sensitivity to Input Data Quality
- Lack of External Factors
- Model Generalization Challenges
- Include Additional Relevant Features
- Explore Advanced LSTM Architectures
- Fine-Tune Hyperparameters
- Integrate External Data Sources
- Develop Real-time Prediction Capability
- T2M : Temperature at 2 meters (in degrees Celsius).
- T_Fahrenheit : Temperature at 2 meters converted to Fahrenheit.
- TS : Surface temperature (in degrees Celsius).
- T2M_MIN : Minimum temperature at 2 meters (in degrees Celsius).
- T2M_MAX : Maximum temperature at 2 meters (in degrees Celsius).
- T2MWET : Wet-bulb temperature at 2 meters (in degrees Celsius).
- WS10M_RANGE : Wind speed at 10 meters range (difference between maximum and minimum) (in meters per second).
- WD50M : Wind direction at 50 meters (in degrees).
- WD10M : Wind direction at 10 meters (in degrees).
- WS10M_MAX : Maximum wind speed at 10 meters (in meters per second).
- RH2M : Relative humidity at 2 meters (percentage).
- MO : Month.
- WS10M : Wind speed at 10 meters (in meters per second).
- T2MDEW : Dew point temperature at 2 meters (in degrees Celsius).
- QV2M : Specific humidity at 2 meters (in grams per kilogram).
- WS50M : Wind speed at 50 meters (in meters per second).
- LON : Longitude.
- DISTRICT_INDEX : Numerical index representing the district.
- LAT : Latitude.
- PS : Surface pressure (in pascals).
- PRECTOTCORR : Corrected total precipitation (in millimeters).
- YEAR : Year.
- T2M_RANGE : Temperature range at 2 meters (in degrees Celsius).
- DY : Day.
- WS10M_MIN : Minimum wind speed at 10 meters (in meters per second).
- Rows: 125,060
- Columns: 25
- Memory Usage: 23.9 MB
LSTM (Long Short-Term Memory) is a type of recurrent neural network (RNN) designed for sequence prediction. In our project, LSTM is applied to time-series meteorological data to learn patterns and predict heat index.
- Meteorological Forecasting
- Public Health Planning
- Urban Planning for Heat Resilience
- Agriculture Heat Stress Monitoring
- Energy Consumption Forecasting
- Outdoor Event Planning
- Captures Long-Term Dependencies
- Handles Time-Series Data Effectively
- Suitable for Complex Patterns
- Avoids Vanishing Gradient Problem
- Allows End-to-End Learning
- Requires Sufficient Data
- May Overfit with Limited Data
- Computationally Intensive
- Hyperparameter Sensitivity
- Limited Interpretability
- Enhance Model Robustness
- Incorporate Additional Features
- Explore Ensemble Approaches
- Integrate Real-Time Data
- Extend to Multiple Locations
-
selected_features :
['T2M', 'T_Fahrenheit', 'TS', 'T2M_MIN', 'T2M_MAX', 'T2MWET', 'WS10M_RANGE', 'DISTRICT_INDEX']
-
input_data :
pd.DataFrame({ 'T2M': [0.325153], 'T_Fahrenheit': [72.91], 'TS': [0.336126], 'T2M_MIN': [0.431715], 'T2M_MAX': [0.305014], 'T2MWET': [0.631997], 'WS10M_RANGE': [0.164191], 'DISTRICT_INDEX': [10], })
-
Output
Predicted HEAT_INDEX: 73.28 Actual HEAT_INDEX: 74.71 Mean Absolute Error: 1.430001 %
-
selected_features :
['T2M','RH2M', 'T2MDEW', 'WD10M']
-
input_data :
pd.DataFrame({ 'T2M': [0.325153], 'RH2M': [0.733267], 'T2MDEW': [0.766538], 'WD10M': [0.614839], })
-
Output
Predicted HEAT_INDEX: 74.79 Actual HEAT_INDEX: 74.71 Mean Absolute Error: 0.080001 %
-
selected_features :
['T2M', 'T_Fahrenheit', 'TS', 'T2M_MIN', 'T2M_MAX', 'T2MWET', 'WS10M_RANGE', 'WD50M', 'WD10M', 'WS10M_MAX', 'RH2M', 'MO', 'WS10M', 'T2MDEW', 'QV2M', 'WS50M', 'LON', 'DISTRICT_INDEX', 'LAT', 'PS', 'PRECTOTCORR', 'YEAR', 'T2M_RANGE', 'DY', 'WS10M_MIN']
-
input_data :
pd.DataFrame({ 'T2M' : [0.325153], 'T_Fahrenheit' : [72.91], 'TS' : [0.336126], 'T2M_MIN' : [0.431715], 'T2M_MAX' : [0.305014], 'T2MWET' : [0.631997], 'WS10M_RANGE' : [0.164191], 'WD50M' : [0.609584], 'WD10M' : [0.614839], 'WS10M_MAX' : [0.175806], 'RH2M' : [0.733267], 'MO' : [1], 'WS10M' : [0.176881], 'T2MDEW' : [0.766538], 'QV2M' : [0.549058], 'WS50M' : [0.208062], 'LON' : [79.293], 'DISTRICT_INDEX' : [10], 'LAT' : [19.366], 'PS' : [0.43377], 'PRECTOTCORR' : [0.031997], 'YEAR' : [2001], 'T2M_RANGE' : [0.367852], 'DY' : [1], 'WS10M_MIN' : [0.139295], })
-
Output
Predicted HEAT_INDEX: 73.8 Actual HEAT_INDEX: 74.71 Mean Absolute Error: 0.909997 %